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18 de may. de 2024 · [Submitted on 18 May 2024] MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection. Ximiao Zhang, Min Xu, Dehui Qiu, Ruixin Yan, Ning Lang, Xiuzhuang Zhou. In the field of medical decision-making, precise anomaly detection in medical imaging plays a pivotal role in aiding clinicians.
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16 de may. de 2024 · Precise data annotation is essential for anomaly-detection tasks, making the training process complex. Domain generalization (DG) is an important approach to enhancing medical image anomaly detection (AD). This paper introduces a novel multimodal anomaly-detection framework called MedicalCLIP.
22 de may. de 2024 · May-Hegglin anomaly (MHA) is an autosomal dominant disorder characterized by various degrees of thrombocytopenia that may be associated with purpura and bleeding; giant platelets containing few...
Hace 2 días · Machine learning-based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications.
18 de may. de 2024 · This paper proposes an innovative approach, MediCLIP, which adapts the CLIP model to few-shot medical image anomaly detection through self-supervised fine-tuning, and achieves state-of-the-art performance in anomaly detection and location compared to other methods.
21 de may. de 2024 · Abstract: Medical anomaly detection is a critical research area aimed at recognizing abnormal images to aid in diagnosis.Most existing methods adopt synthetic anomalies and image restoration on normal samples to detect anomaly. The unlabeled data consisting of both normal and abnormal data is not well explored.
